NumPy - Thinking in Arrays
NumPy - Thinking in Arrays
May 24, 2017
NumPy
Motivation
At the core of most computational physics problems lives an array. Arrays are a natural way to describe numerical and discretized problems. Because geometry can be dissected into tetrahedrons (pyramids) or hexahedrons (cubes) and arrays can then be used to represent scalar or vector values of each point in three dimensional space. Operations on arrays can furthermore be used to represent or approximate calculus operations, such as integration or differentiation. For a computer an array is just a contiguous block of memory where every element has the same type and layout.
NumPy
Motivation
Every programming language that is serious about scientific computing has a notion of a array data language. Some languages such as MATLAB or Mathematica are centered around the array data language. In Fortran arrays are first class citizens as well.
NumPy
Motivation
Every programming language that is serious about scientific computing has a notion of a array data language. Some languages such as MATLAB or Mathematica are centered around the array data language. In Fortran arrays are first class citizens as well.
Python has NumPy!
NumPy is easy to learn, intuitive to use, and orders of magnitudes faster than using pure Python for array-based operations.
NumPy
Arrays
The basic data type that NumPy provides is the n-dimensional array class ndarray. You can create an array in NumPy using the array() function.
In [1]: import numpy as np In [2]: np.array([6, 28, 496, 8218]) Out [2]: array([ 6, 28, 496, 8218])
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